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- 1. USING SIGOPT TO TUNE DEEP LEARNING MODELS WITH NERVANA CLOUD Scott Clark Co-founder and CEO of SigOpt scott@sigopt.com @DrScottClark
- 2. TRIAL AND ERROR WASTES EXPERT TIME ● Deep Learning is extremely powerful ● Tuning Deep Learning systems is extremely non-intuitive
- 3. UNRESOLVED PROBLEM IN ML https://www.quora.com/What-is-the-most-important-unresolved-problem-in-machine-learning-3 What is the most important unresolved problem in machine learning? “...we still don't really know why some configurations of deep neural networks work in some case and not others, let alone having a more or less automatic approach to determining the architectures and the hyperparameters.” Xavier Amatriain, VP Engineering at Quora (former Director of Research at Netflix)
- 4. TUNING DEEP LEARNING MODELS Big Data Deep Learning System With tunable parameters Expertise
- 5. TUNING DEEP LEARNING MODELS Big Data Metics Optimally suggests new parameters Objective New parameters Expertise Deep Learning System With tunable parameters
- 6. TUNING DEEP LEARNING MODELS Big Data Metics Optimally suggests new parameters Objective New parameters Better Results Expertise Deep Learning System With tunable parameters
- 7. COMMON APPROACH Random Search for Hyper-Parameter Optimization, James Bergstra et al., 2012 1. Random search or grid search 2. Expert defined grid search near “good” points 3. Refine domain and repeat steps - “grad student descent”
- 8. COMMON APPROACH ● Expert intensive ● Computationally intensive ● Finds potentially local optima ● Does not fully exploit useful information Random Search for Hyper-Parameter Optimization, James Bergstra et al., 2012 1. Random search or grid search 2. Expert defined grid search near “good” points 3. Refine domain and repeat steps - “grad student descent”
- 9. … the challenge of how to collect information as efficiently as possible, primarily for settings where collecting information is time consuming and expensive. Prof. Warren Powell - Princeton What is the most efficient way to collect information? Prof. Peter Frazier - Cornell How do we make the most money, as fast as possible? Me - @DrScottClark OPTIMAL LEARNING
- 10. ● Optimize some Overall Evaluation Criterion (OEC) ○ Loss, Accuracy, Likelihood, Revenue ● Given tunable parameters ○ Hyperparameters, feature parameters ● In an efficient way ○ Sample function as few times as possible ○ Training on big data is expensive BAYESIAN GLOBAL OPTIMIZATION Details at https://sigopt.com/research
- 11. EXAMPLE: TUNING DNN CLASSIFIERS CIFAR10 Dataset ● Photos of objects ● 10 classes ● Metric: Accuracy ○ [0.1, 1.0] Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009.
- 12. ● All convolutional neural network ● Multiple convolutional and dropout layers ● Hyperparameter optimization mixture of domain expertise and grid search (brute force) USE CASE: ALL CONVOLUTIONAL http://arxiv.org/pdf/1412.6806.pdf
- 13. EXAMPLE: NCLOUD/NEON ● epochs: “number of epochs to run fit” - int [1,∞] ● learning rate: influence on current value of weights at each step - double (0, 1] ● momentum coefficient: “the coefficient of momentum” - double (0, 1] ● weight decay: parameter affecting how quickly weight decays - double (0, 1] ● depth: parameter affecting number of layers in net - int [1, 20(?)] ● gaussian scale: standard deviation of initialization normal dist. - double (0,∞] ● momentum step change: mul. amount to decrease momentum - double (0, 1] ● momentum step schedule start: epoch to start decreasing momentum - int [1,∞] ● momentum schedule width: epoch stride for decreasing momentum - int [1,∞] Many tunable parameters... ...optimal values non-intuitive
- 14. COMPARATIVE PERFORMANCE ● Expert baseline: 0.8995 ○ (using neon) ● SigOpt best: 0.9011 ○ 1.6% reduction in error rate ○ No expert time wasted in tuning
- 15. USE CASE: DEEP RESIDUAL http://arxiv.org/pdf/1512.03385v1.pdf ● Explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions ● Variable depth ● Hyperparameter optimization mixture of domain expertise and grid search (brute force)
- 16. COMPARATIVE PERFORMANCE Standard Method ● Expert baseline: 0.9339 ○ (from paper) ● SigOpt best: 0.9343 ○ Found after 17 trials ○ 0.61% reduction in error rate ○ No expert time wasted in tuning
- 17. Questions? scott@sigopt.com @DrScottClark https://sigopt.com @SigOpt
- 18. TRY OUT SIGOPT FOR FREE https://sigopt.com/get_started ● Quick example and intro to SigOpt ● No signup required ● Visual and code examples https://sigopt.com/text-classifier ● Jupyter Notebook ● Use SigOpt to tune feature and model parameters ● Detailed walkthrough with code
- 19. MORE EXAMPLES https://github.com/sigopt/sigopt-examples Examples of using SigOpt in a variety of languages and contexts. Tuning Machine Learning Models (with code) A comparison of different hyperparameter optimization methods. Using Model Tuning to Beat Vegas (with code) Using SigOpt to tune a model for predicting basketball scores. Learn more about the technology behind SigOpt at https://sigopt.com/research
- 20. HOW DOES IT WORK? 1. User reports data 2. SigOpt builds statistical model (Gaussian Process) 3. SigOpt finds the points of highest Expected Improvement 4. SigOpt suggests best parameters to test next 5. User tests those parameters and reports results to SigOpt 6. Repeat
- 21. HOW DOES IT WORK? 1. User reports data 2. SigOpt builds statistical model (Gaussian Process) 3. SigOpt finds the points of highest Expected Improvement 4. SigOpt suggests best parameters to test next 5. User tests those parameters and reports results to SigOpt 6. Repeat
- 22. HOW DOES IT WORK? 1. User reports data 2. SigOpt builds statistical model (Gaussian Process) 3. SigOpt finds the points of highest Expected Improvement 4. SigOpt suggests best parameters to test next 5. User tests those parameters and reports results to SigOpt 6. Repeat
- 23. HOW DOES IT WORK? 1. User reports data 2. SigOpt builds statistical model (Gaussian Process) 3. SigOpt finds the points of highest Expected Improvement 4. SigOpt suggests best parameters to test next 5. User tests those parameters and reports results to SigOpt 6. Repeat
- 24. HOW DOES IT WORK? 1. User reports data 2. SigOpt builds statistical model (Gaussian Process) 3. SigOpt finds the points of highest Expected Improvement 4. SigOpt suggests best parameters to test next 5. User tests those parameters and reports results to SigOpt 6. Repeat
- 25. HOW DOES IT WORK? 1. User reports data 2. SigOpt builds statistical model (Gaussian Process) 3. SigOpt finds the points of highest Expected Improvement 4. SigOpt suggests best parameters to test next 5. User tests those parameters and reports results to SigOpt 6. Repeat
- 26. GPs: FUNCTIONAL VIEW
- 27. overfit good fit underfit GPs: FITTING THE GP